Modified resampling strategy for extreme values in imbalanced air pollution data using moving block bootstrapping approach with relevance weighting (MBB-RW).

Air pollution datasets typically exhibit a right-skewed distribution. These conditions are caused by the presence of extreme events leading to imbalanced data distribution. This imbalanced regression poses a notable challenge in predictive modeling, as the models tend to be biased towards frequent normal events while underperforming extreme events. Therefore, to address this issue, the resampling approach is crucial in handling these extreme events to improve model performance. In this study, a modified resampling strategy, called Moving Block Bootstrapping with Relevance Weighting (MBB-RW), is proposed to address imbalanced regression problems. By integrating MBB with relevance weighting, the time-series dependence of air pollution data is preserved while placing greater emphasis on extreme events. The findings demonstrate that MBB-RW can mitigate data imbalance and enhance model prediction accuracy for extreme events. These enhancements are evident in the performance of the Extreme Gradient Boosting (XGBoost) model in predicting PM